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Research Article | Open Access

Multi-scale Jones polynomial and persistent Jones polynomial for knot data analysis

Ruzhi Song1,3Fengling Li1( )Jie Wu2,3Fengchun Lei1Guo-Wei Wei4,5,6
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, Liaoning, China
School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, Hebei, China
Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
Department of Mathematics, Michigan State University, 426 Auditorium Road, East Lansing, MI 48824, USA
Department of Biochemistry and Molecular Biology, Michigan State University, 426 Auditorium Road, East Lansing, MI 48824, USA
Department of Electrical and Computer Engineering, Michigan State University, 426 Auditorium Road, East Lansing, MI 48824, USA
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Abstract

Many structures in science, engineering, and art can be viewed as curves in 3-space. The entanglement of these curves plays a crucial role in determining the functionality and physical properties of materials. Many concepts in knot theory provide theoretical tools to explore the complexity and entanglement of curves in 3-space. However, classical knot theory focuses on global topological properties and lacks the consideration of local structural information, which is critical in practical applications. In this work, two localized models based on the Jones polynomial were proposed, namely, the multi-scale Jones polynomial and the persistent Jones polynomial. The stability of these models, especially the insensitivity of the multi-scale and persistent Jones polynomial models to small perturbations in curve collections, was analyzed, thus ensuring their robustness for real-world applications.

CLC number: 57K10, 92C10

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AIMS Mathematics
Pages 1463-1487

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Cite this article:
Song R, Li F, Wu J, et al. Multi-scale Jones polynomial and persistent Jones polynomial for knot data analysis. AIMS Mathematics, 2025, 10(1): 1463-1487. https://doi.org/10.3934/math.2025068

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Received: 16 November 2024
Revised: 04 January 2025
Accepted: 13 January 2025
Published: 15 January 2025
©2025 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)